import cmasher as cmr
import colorcet as cc
import numpy as np
import matplotlib as mpl
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
%matplotlib inline
from matplotlib.ticker import MaxNLocator
import pandas as pd
import re
from scipy.optimize import curve_fit
import seaborn as sns
import string

corruption_data = pd.read_csv(r'CPI_data_2018.csv')
religiosity_data = pd.read_csv(r'Religiosity_Pew2018.csv')
religiosity_data.dropna(axis='index', how='any', inplace=True)

# Merge the two dataframes on the "country names" column
corruption_religiosity = pd.merge(corruption_data, religiosity_data, on="Country Name")
#pd.options.display.max_rows = None
corruption_religiosity.sort_values(['Corruption Perception Index', 'Think religion is very important'], axis='index', ascending=True, inplace=False)

	Country Name	Corruption Perception Index	Religious Affiliation	Attend weekly	Pray daily	Think religion is very important
41	Guinea Bissau	16	100	81.0	83.0	91
0	Afghanistan	16	100	61.0	96.0	92
97	Venezuela	18	93	26.0	47.0	67
46	Iraq	18	100	42.0	87.0	82
18	Chad	19	97	77.0	83.0	86
...	...	...	...	...	...	...
70	Norway	84	57	7.0	18.0	19
87	Switzerland	85	79	11.0	8.0	9
34	Finland	85	78	4.0	18.0	10
86	Sweden	85	58	6.0	11.0	10
26	Denmark	88	70	3.0	10.0	9

99 rows × 6 columns

Retrieve the Fit and Correlation Coefficient of Two Variables

def regression_line(x, a, b):
    # Curve fitting function
    return a*x + b

# Curve fitting
ykey_string = r'Think religion is very important'
x = corruption_religiosity['Corruption Perception Index'].to_numpy()
y = corruption_religiosity[ykey_string].to_numpy()
params = curve_fit(regression_line, x, y)
inv_params = curve_fit(regression_line, y, x)
a = params[0]
inv_a = inv_params[0]
r_squared = a[0]*inv_a[0]
error_density = np.sqrt(np.diag(params[1]))[0]
x_fit = np.linspace(0, x.max(), 100)
y_fit = a[0]*x_fit + a[1]

Plotting the Scatter Plot of the Relationship

sns.set(style="ticks", rc={'axes.facecolor': 'white','axes.edgecolor': 'black',\
                           'axes.grid': True,'grid.color': '#b0b0b0','grid.linestyle': '-'})
fig, ax = plt.subplots(figsize=(8,10), dpi=100)
ax.set_xlim(0,100)
ax.set_ylim(0,100)

# Plotting
scatter_religion = ax.scatter('Corruption Perception Index', ykey_string, s=50, c='Pray daily', marker='o', cmap=cc.cm.CET_R2, vmin=0, vmax=100,
                              alpha=1.0, linewidths=1.0, edgecolors='xkcd:dark grey', plotnonfinite=False, data=corruption_religiosity, zorder=4)
regplot_line_kws = {'linewidth':1.5, 'color':'xkcd:pale red', 'linestyle':'--', 'zorder':3}
sns_regplot = sns.regplot(data=corruption_religiosity, x="Corruption Perception Index", y=ykey_string,
            ax=ax, scatter=False, truncate=False, line_kws=regplot_line_kws)
# Access the confidence interval plot
ci_plot = sns_regplot.get_children()[2]
# Set the z-order of the confidence interval plot
ci_plot.set_zorder(3)  # Set the desired z-order (higher values are drawn on top)

ax.grid(True, alpha=0.5, zorder=1)
ax.set_aspect('equal')
ax.set_xlabel('Indeks Persepsi Korupsi (0:korup, 100:bersih)', fontsize=14, fontfamily='Calibri', fontweight='bold', labelpad=8.0)
ax.set_ylabel('% orang yang berpikir "agama itu penting"', fontsize=14, fontfamily='Calibri', fontweight='bold')

# Colorbar
pray_colorbar = fig.colorbar(scatter_religion, location='bottom', orientation='horizontal', shrink=0.55, pad=0.13)
pray_colorbar.set_label('% orang yang beribadah setiap hari')
pray_colorbar.ax.xaxis.set_major_locator(MaxNLocator(integer=True))

# Title
if np.abs(np.round(a[0],0))==1:
    if np.sign(np.round(a[0],0))==1:
        title_txt = 'Level Religiusitas vs Indeks Persepsi Korupsi' + '\n' + r'$\mathrm{y = x +'+'{:.0f}'.format(a[1])\
        +  r';\;\;\;R^2 =' + '{:.3f}'.format(r_squared) +r'}$'
    if np.sign(np.round(a[0],0))==-1:
        title_txt = 'Level Religiusitas vs Indeks Persepsi Korupsi' + '\n' + r'$\mathrm{y = -x +'+'{:.0f}'.format(a[1])\
        +  r';\;\;\;R^2 =' + '{:.3f}'.format(r_squared) +r'}$'
else:
    title_txt = 'Level Religiusitas vs Indeks Persepsi Korupsi' + '\n' + r'$\mathrm{y =' +\
    '{:.0f}'.format(a[0])+r'x+'+'{:.0f}'.format(a[1])\
    +  r';\;\;\;R^2 =' + '{:.3f}'.format(r_squared) +r'}$'
ax.set_title(title_txt, fontfamily='Proxima Nova Alt', fontsize = 16, fontweight='bold')

# Annotations to the right
countries_list_right = np.array(['Indonesia', 'Botswana', 'Jordan', 'Senegal', 'Rwanda', 'Turkey',
                                 'Serbia', 'Chile', 'Georgia', 'Costa Rica', 'Canada', 'Norway', 'Denmark',
                                 'Bangladesh', 'Paraguay', 'Greece', 'Lithuania', 'Uruguay', 'Portugal',
                                 'Latvia', 'Croatia'])
select_countries_right = corruption_religiosity[corruption_religiosity['Country Name'].isin(countries_list_right)].copy()
loc_text = select_countries_right[['Corruption Perception Index', ykey_string]].to_numpy()
countries_list_right = select_countries_right['Country Name'].to_numpy()

idx = 0
for names in countries_list_right:
    ax.annotate(names, xy=(loc_text[idx,0], loc_text[idx,1]), xycoords='data', xytext=(5, -2), textcoords='offset points',
                color='xkcd:black', fontstyle='italic', fontfamily='Segoe UI', fontsize=10, backgroundcolor='white', zorder=2)
    idx += 1

ax.plot('Corruption Perception Index',ykey_string,color='xkcd:black',marker='.',linewidth=0,markersize=5,data=select_countries_right, zorder=5)

# Annotations to the left
countries_list_left = np.array(['Ethiopia', 'Venezuela', 'Panama', 'South Korea', 'Azerbaijan', 'China', 'Russia',
                                'Uzbekistan', 'Iraq', 'Afghanistan', 'Pakistan', 'Mexico', 'Lebanon', 'Estonia',
                                'Czech Republic', 'Kazakhstan', 'Poland', 'Ireland', 'Tajikistan', 'Israel'])
select_countries_left = corruption_religiosity[corruption_religiosity['Country Name'].isin(countries_list_left)].copy()
loc_text = select_countries_left[['Corruption Perception Index', ykey_string]].to_numpy()
countries_list_left = select_countries_left['Country Name'].to_numpy()

idx = 0
for names in countries_list_left:
    ax.annotate(names, xy=(loc_text[idx,0], loc_text[idx,1]), xycoords='data', xytext=(-5, -2), textcoords='offset points',
                color='xkcd:black', fontstyle='italic', fontfamily='Segoe UI', fontsize=10, backgroundcolor='white', zorder=2, horizontalalignment='right')
    idx += 1

ax.plot('Corruption Perception Index',ykey_string,color='xkcd:black',marker='.',linewidth=0,markersize=5,data=select_countries_left, zorder=5)

[<matplotlib.lines.Line2D at 0x2a535631c00>]

import plotly.express as px

# Create scatter plot using Plotly
fig = px.scatter(corruption_religiosity, x='Corruption Perception Index', y='Think religion is very important',
                 text='Country Name', hover_data=corruption_religiosity.columns,  width=600, height=800)

# Update layout to display text when clicking on points
fig.update_traces(textposition='top center', mode='markers+text')

# Show plot
fig.show()